Predictive model for corrosion hazard of buried metallic structure caused by stray current in the subway

Abstract

Purpose The purpose of this paper is to predict quantitative level of stray current leaking to the buried metallic structure by establishing convolution neural network (CNN) model. Design/methodology/approach First, corrosion experimental system of buried metallic structure is established. The research object of this paper is the polarization potential within 110 min, CNN model is used to predict the quantitative level of stray current leakage using the data from corrosion experimental system further. Finally, results are compared with the method using BP neural network. Findings Results show that the CNN model has better predictive effect and shorter prediction time than the BP model, the accuracy of which is 82.5507 per cent, and the prediction time is shortened by more than 10 times. Originality/value The established model can be used to forecast the level of stray current leakage in the subway system effectively, which provides a new theoretical basis for evaluating the stray current corrosion hazard of buried metallic structure.

Application of artificial intelligence (AI) in the area of corrosion protection

“…Compared with traditional methods, AI shows unique advantages (Boucherit et al , 2022; Boucherit et al , 2019; Boucherit and Arbaoui, 2021). Its efficient and in-depth data processing method can rapidly advance the research process (Butler et al , 2018; Kumari et al , 2017; Wang et al , 2019; Zhao et al , 2021).…”